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fix: stop word filtering in entity scoring — common words polluted rankings

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'the', 'full', 'text', 'proposal' etc. were matching irrelevant entities.
Robin Hanson record ranked #2 behind Drift because Drift matched 'the' and
'proposal' in its name. Now only meaningful tokens (>=3 chars, not stop
words) contribute to entity scoring.

Pentagon-Agent: Epimetheus <3D35839A-7722-4740-B93D-51157F7D5E70>
This commit is contained in:
m3taversal 2026-03-23 17:44:06 +00:00
parent 089b4609d5
commit f77fd229d6
1 changed files with 17 additions and 5 deletions
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22
telegram/kb_retrieval.py
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@ -430,6 +430,17 @@ def retrieve_context(query: str, repo_dir: str, index: KBIndex | None = None,
# ─── Scoring ──────────────────────────────────────────────────────────
_STOP_WORDS = frozenset({
"the", "for", "and", "but", "not", "you", "can", "has", "are", "was",
"its", "all", "had", "her", "one", "our", "out", "new", "now", "old",
"see", "way", "may", "say", "she", "two", "how", "did", "get", "put",
"give", "me", "ok", "full", "text", "what", "about", "tell", "this",
"that", "with", "from", "have", "more", "some", "than", "them", "then",
"into", "also", "just", "your", "been", "here", "will", "does", "know",
"please", "think",
})
def _score_entity(query_lower: str, query_tokens: list[str], entity: dict) -> float:
"""Score an entity against a query. Higher = more relevant."""
name_lower = entity["name"].lower()
@ -437,9 +448,10 @@ def _score_entity(query_lower: str, query_tokens: list[str], entity: dict) -> fl
aliases = entity.get("aliases", [])
score = 0.0
for token in query_tokens:
if len(token) < 2:
continue
# Filter out stop words — only score meaningful tokens
meaningful_tokens = [t for t in query_tokens if t not in _STOP_WORDS and len(t) >= 3]
for token in meaningful_tokens:
# Name match (highest signal)
if token in name_lower:
score += 3.0
@ -451,8 +463,8 @@ def _score_entity(query_lower: str, query_tokens: list[str], entity: dict) -> fl
score += 0.5
# Boost multi-word name matches (e.g. "robin hanson" in entity name)
if len(query_tokens) >= 2:
bigrams = [f"{query_tokens[i]} {query_tokens[i+1]}" for i in range(len(query_tokens) - 1)]
if len(meaningful_tokens) >= 2:
bigrams = [f"{meaningful_tokens[i]} {meaningful_tokens[i+1]}" for i in range(len(meaningful_tokens) - 1)]
for bg in bigrams:
if bg in name_lower:
score += 5.0